The field of envisioning information has exploded recently. For thousands of years, data visualization did not exist. Since its inception (in the 1600s), people have struggled to represent more than two dimensions. With technological advancements, internet media provides interactivity, where viewers can be actively involved with data visualization in way that effectively represents more than 2 dimensions.

With all our technological capabilities, as well as an overwhelming amount of available data, there is almost no limit to how much control we can give the user over the visualization. We could display raw data and let viewers explore to find their own insights, however at some point gives them too much freedom & overwhelms them, and the visualization is no longer effective. Instead, we should guide viewers & empower them.

So, how do we keep our big data visualizations exploratory, yet empowering? Start with a simple end goal of the visualization, create an uninformative graph, next add details & functionality until the graph is too confusing, and finally scale back & remove anything unnecessary. Two leading voices in the field of information visualization whose advice is important to heed are Alan Cooper, a father of interactive design, and Edward Tufte, a father of information visualization. For all those creating or critiquing data visualizations, here are five crucial checkpoints, with an example to demonstrate the importance of each.

Start with The End in Mind

Define what the product will do before you design how the product will do it.

When you start thinking about a new data visualization, don’t get caught up in the details of the presentation. Clearly define what you are trying to accomplish. Are you providing an interface for a viewer to explore their data? What are viewers most interested in, and how can you make that prominent? Starting with the end in mind will let you know what to focus on.

In other words, know what those using the platform will be asking of the data before you design the way in which your design answers that question.

Figure 1: We start with a simple graph to show monthly ticket sales over time. Why is this graph limited? Motivate the reader.

Be Careful When Parsing Data

There are right & wrong ways to show data; there are displays that reveal the truth and displays that do not.

With large data sets, you need to select what to show viewers, but be careful about how you choose what to show. Collapse over the wrong interval and you’ll distort the data beyond recognition. Showing monthly data instead of daily is sometimes a good choice, but not for every data set or end goal.

Again, know the questions viewers will ask before you decide what not to display.

Give context

…quantitative presentation should answer the question, “Compared to what?

There are no insights to be learned from one data point. Knowing that 29% of ticket buyers purchased last month won’t tell you much. We only gain insights when we have something to compare. To add that context, in this example, we could contrast ticket sales during the week with ticket sales on the weekend, as you see below.

Figure 3: We highlighted weekends, showing that we sell more tickets on weekdays than on weekends.

Know your tools

Escaping this flatland is the essential task of envisioning information–for all of the interesting worlds (physical, biological, imaginary, human) that we seek to understand are inevitable and happily multivariate in nature. Not flatlands.

This is 2014, and that means we have buckets of amazing tools to use while envisioning data. The trick is in knowing which ones to use. Sometimes, a static image is the best way to get the message across. But with large, dynamic data sets, it’s helpful to use interactions to our advantage. Interactions can add many affordances, such as to progressively disclose more information (e.g. tooltips), show a different granularity (e.g. zooming), show a different data set (e.g. panning a map), or any kind of helpful manipulation (e.g. easy comparisons). Don’t give the viewer so much control that the data loses its ability to guide the user to insight. Do empower them, though, by giving them control over the pace, sequence, direction and focus.

Figure 4: We add tooltips for more information for each data point & brushing (along the bottom) to allow users to zoom in & out of specific times.

Substance over sparkle

No matter how cool you interface is, less of it would be better.

Do you really need that graph to zoom in on page load? Animations should be reserved for depicting actual motion or relationships between elements. Bright colors should be reserved to indicate importance or intensity. Sparkly things are fun to look at, but won’t help you understand patterns within your data. They only serve as distraction.

Our screens are cluttered with desktop folders, email notifications and browser navigation. None of these is important when we are trying to find insights from data. Keep your visualization as clean as possible and include nothing without purpose. Additionally, evaluate the priority of every element: is a dark grid the most important element, or should we reduce the contrast, for example?

Figure 5: We took away distracting grid lines, gradients, buttons, & strokes to focus on the content, instead of the graph.

Follow these steps in every one of your data visualizations, whether you create them, use them or are having to decide between different platforms that market them. In doing so, you’ll find unknown adjacencies and insights like never before, and be able to use those insights to increase ROI or customer satisfaction. No data visualization exists merely for entertainment. All visualizations should lead a user to insights, make their job easier, or what have you.

These guidelines as set up by two leaders of the data visualization industry will guide you in creating, using and investing in visualizations that are intuitive, effective and actionable.

To see how Umbel utilizes these guidelines in our own interactive data visualizations, contact us for a demo.

Amelia combines her experience with data visualization, Psychology, and coding with her lifelong passion for programming and design. Amelia graduated from Trinity College in Connecticut with a B.S. in Neuroscience and Psychology.